This invention relates generally to image reconstruction and more particularly, to improving both temporal resolution and spatial resolution of a reconstructed image.
CT cardiac imaging is one of the most recent technological advancements in CT imaging; however at least some known methods of CT cardiac imaging are limited due to motion of the heart during the CT scan. As such, it is essential to collect the CT data when the motion of the heart is minimal. Therefore, at least some known methods of CT cardiac imaging collect data within a narrow temporal window that corresponds with a specific phase of the heart cycle.
Specifically, in some known CT cardiac imaging methods, filtered-backprojection (FBP) image reconstruction is used to reconstruct projection data that spans a sufficiently wide angular range. The data is grouped or merged into L datasets of measured projections such that each frame ym of the corresponding dataset can be reconstructed. For example, if L=2, a first dataset z1 would equal {y1 . . . yM/2} and a second dataset z2 would equal {yM/2+1 . . . yM}, wherein M denotes the total number of projection views. Typically, datasets z1 and z2 would be defined based upon an EKG signal. Frame f1 is then reconstructed from dataset z1 and frame f2 is reconstructed from dataset z2. As such, an image of the heart can be reconstructed for each phase of the cardiac cycle. Further, iterative reconstruction methods can also be used to reconstruct f1 from dataset z1 and f2 from dataset z2. However, using conventional iterative reconstruction to such grouped datasets does not improve temporal resolution relative to FBP image reconstruction because the grouping that defines the datasets {z1} determines the temporal resolution. In particular, conventional iterative methods and FBP methods require complete or nearly complete sets of projection views and this often requires longer time intervals over which object motion may occur.